Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean

Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60–75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r=−0.67 to −0.91 in ADP; r=−0.55 to −0.72 in DDP) and seed yield (r=0.51 to 0.73 in ADP; r=0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r=0.28–0.88). Finally, lower resolution satellite images (0.05–5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r=0.52) and biomass (r=−0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping

Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs \u3e 0.60, coefficient of determination (R2): 0.50–0.56, P \u3c 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments

Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Global plant genetics research efforts have focused on developing high yielding, stress tolerant, and disease resistant row and field crop varieties that are more efficient in their use of agronomic inputs (water, nutrients, pesticides, etc.). Until recently, a key bottleneck in such research was the lack of high-throughput sensing technologies for effective and rapid evaluation of expressed phenotypes under field conditions for holistic data-driven decision making and variety selection. This review focuses on technological aspects of integrating unmanned aerial vehicles with imaging systems to enhance field phenotyping capabilities. The state-of-the-art of unmanned aerial vehicle technology for various applications including crop emergence, vigor, and characterization of yield potential of row and field crops has been reviewed. The potential of using aerial imaging to evaluate resistance/susceptibility to biotic and abiotic stress for crop breeding and precision production management has been discussed along with future perspectives and developments.Keywords: High-throughput field phenomics, Aerial imaging, Crop breeding, Data minin

Using UAV-Based Imagery to Determine Volume, Groundcover, and Growth Rate Characteristics of Lentil (Lens culinaris Medik.)

Plant growth rate is an essential phenotypic parameter for crop physiologists and plant breeders to understand in order to quantify potential crop productivity based on specific stages throughout the growing season. While plant growth rate information can be attained though manual collection of biomass, this procedure is rarely performed due to the prohibitively large effort and destruction of plant material that is required. Unmanned Aerial Vehicles (UAVs) offer great potential for rapid collection of imagery which can be utilized for quantification of plant growth rate. In this study, six diverse lines of lentil were grown in three replicates of microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery of each biomass collection time point was collected from a UAV and utilized to produce stitched two-dimensional orthomosaics and three-dimensional point clouds. Analysis of this imagery produced quantification of groundcover and vegetation volume on an individual plot basis. Comparison with manually-measured above-ground biomass suggests strong correlation, indicating great potential for UAVs to be utilized in plant breeding programs for evaluation of groundcover and vegetation volume. Nonlinear logistic models were fit to multiple data collection points throughout the growing season. The growth rate and G50, which is the number of growing degree days (GDD) required to accumulate 50 % of maximum growth, parameters of the model are capable of quantifying growth rate, and have potential utility in plant research and plant breeding programs. Predicted maximum volume was identified as a potential proxy for whole-plot biomass measurement. Six new phenotypes have been described that can be accurately and efficiently collected from field trials with the use of UAV’s or other overhead image-collection systems. These phenotypes are; Area Growth Rate, Area G50, Area Maximum Predicted Growth, Volume Growth Rate, Volume G50, and Volume Maximum Predicted Growth

Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R2) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance

Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58–0.86), to anthesis (DA, r = 0.59–0.85) and to maturity (r = 0.67–0.95), grain-filling duration (GFD, r = 0.54–0.74), plant height (r = 0.62–0.69), number of grains per spike (NGS, r = 0.41–0.64), and thousand kernel weight (TKW, r = 0.37–0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.info:eu-repo/semantics/publishedVersio

Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials.

Growing demands for increased global yields are driving researchers to develop improved crops, capable of securing higher yields in the face of significant challenges including climate change and competition for resources. However, abilities to measure favourable physical characteristics (phenotypes) of key crops in response to these challenges is limited. For crop breeders and researchers, current abilities to phenotype field-based experiments with sufficient precision, resolution and throughput is restricting any meaningful advances in crop development. This PhD thesis presents work focused on the development and evaluation of Unmanned Aerial Vehicles (UAVs) in combination with remote sensing technologies as a solution for improved phenotyping of field-based crop experiments. Chapter 2 presents first, a review of specific target phenotypic traits within the categories of crop morphology and spectral reflectance, together with critical review of current standard measurement protocols. After reviewing phenotypic traits, focus turns to UAVs and UAV specific technologies suitable for the application of crop phenotyping, including critical evaluation of both the strengths and current limitations associated with UAV methods and technologies, highlighting specific areas for improvement. Chapter 3 presents a published paper successfully developing and evaluating Structure from Motion photogrammetry for accurate (R2 ≥ 0.93, RMSE ≤ 0.077m, and Bias ≤ -0.064m) and temporally consistent 3D reconstructions of wheat plot heights. The superior throughput achieved further facilitated measures of crop growth rate through the season; whilst very high spatial resolutions highlighted both the inter- and intra-plot variability in crop heights, something unachievable with the traditional manual ruler methods. Chapter 4 presents published work developing and evaluating modified Commercial ‘Off the Shelf’ (COTS) cameras for obtaining radiometrically calibrated imagery of canopy spectral reflectance. Specifically, development focussed on improving application of these cameras under variable illumination conditions, via application of camera exposure, vignetting, and irradiance corrections. Validation of UAV derived Normalised Difference Vegetation Index (NDVI) against a ground spectrometer from the COTS cameras (0.94 ≤ R2 ≥ 0.88) indicated successful calibration and correction of the cameras. The higher spatial resolution obtained from the COTS cameras, facilitated the assessment of the impact of background soil reflectance on derived mean Normalised Difference Vegetation Index (NDVI) measures of experimental plots, highlighting the impact of incomplete canopy on derived indices. Chapter 5 utilises the developed methods and cameras from Chapter 4 to assess the impact of nitrogen fertiliser application on the formation and senescence dynamics of canopy traits over multiple growing seasons. Quantification of changes in canopy reflectance, via NDVI, through three select trends in the wheat growth cycle were used to assess any impact of nitrogen on these periods of growth. Results showed consistent impact of zero nitrogen application on crop canopies within all three development phases. Additional results found statistically significant positive correlations between quantified phases and harvest metrics (e.g. final yield), with greatest correlations occurring within the second (Full Canopy) and third (Senescence) phases. Chapter 6 focuses on evaluation of the financial costs and throughput associated with UAVs; with specific focus on comparison to conventional methods in a real-world phenotyping scenario. A ‘cost throughput’ analysis based on real-world experiments at Rothamsted Research, provided quantitative assessment demonstrating both the financial savings (£4.11 per plot savings) and superior throughput obtained (229% faster) from implementing a UAV based phenotyping strategy to long term phenotyping of field-based experiments. Overall the methods and tools developed in this PhD thesis demonstrate UAVs combined with appropriate remote sensing tools can replicate and even surpass the precision, accuracy, cost and throughput of current strategies

Thermography to assess grapevine status and traits opportunities and limitations in crop monitoring and phenotyping – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoClimate change and the increasing water shortage pose increasing challenges to agriculture and viticulture, especially in typically dry and hot areas such as the Mediterranean and demand for solutions to use water resources more effectively. For this reason, new tools are needed to precisely monitor water stress in crops such as grapevine in order to save irrigation water, while guaranteeing yield. Imaging technologies and remote sensing tools are becoming more common in agriculture and plant/crop science research namely to perform phenotyping/selection or for crop stress monitoring purposes. Thermography emerged as important tool for the industry and agriculture. It allows detection of the emitted infrared thermal radiation and conversion of infrared radiation into temperature distribution maps. Considering that leaf temperature is a feasible indicator of stress and/or stomatal behavior, thermography showed to be capable to support characterization of novel genotypes and/or monitor crop’s stress. However, there are still limitations in the use of the technique that need to be minimized such as the accuracy of thermal data due to variable weather conditions, limitations due to the high costs of the equipment/platforms and limitations related to image analysis and processing to extract meaningful thermal data. This work revises the role of remote sensing and imaging in modern viticulture as well as the advantages and disadvantages of thermography and future developments, focusing on viticultureN/

Field phenomics:will it enable crop improvement?

Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders’ needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders